Nanoplastics (NPs; <1000 nm) are persistent soil contaminants that suppress extracellular enzyme activity, the biochemical engine of terrestrial nutrient cycling. Despite a rapidly expanding primary literature, no comprehensive meta-analysis has systematically integrated quantitative effect-size synthesis with interpretable machine learning (ML) approaches to identify and rank the physicochemical drivers of NP-induced soil enzymatic toxicity. Following the PRISMA 2020 statement, we systematically searched four databases (Web of Science, Scopus, PubMed, Google Scholar) from database inception through December 2024 and extracted 413 effect sizes from 113 peer-reviewed studies. Hedges’ g was estimated using three-level random-effects models with restricted maximum likelihood (REML) estimation implemented in the metafor package. Three supervised ML algorithms—random forest (RF), gradient boosting machines (GBMs), and support vector regression (SVR)—were trained using 18 study-level predictors derived from the complete meta-analytic dataset, and SHapley Additive exPlanations (SHAP) were applied to quantify and rank the relative importance of individual predictors. The overall meta-analysis demonstrated a significant inhibitory effect of NPs on soil enzyme activity (Hedges’ g = −0.94; 95% CI: −1.14 to −0.73; k = 413; I² = 78.4%; τ² = 0.412). Among the evaluated enzymes, dehydrogenase activity exhibited the greatest inhibition (g = −1.12), whereas polystyrene nanoplastics produced the strongest adverse effects (g = −1.15). Particles smaller than 100 nm caused approximately 2.6-fold greater inhibition than particles larger than 500 nm, and dose–response meta-regression identified a nonlinear increase in toxicity at concentrations exceeding 200 mg kg⁻¹. The RF model demonstrated the highest predictive performance, explaining 73% of the variance in an independent testing dataset (R² = 0.73; test set n = 83). SHAP analysis identified particle diameter as the most influential predictor, revealing an approximate critical threshold of 150 nm, below which inhibitory effects increased markedly. Higher soil organic carbon concentrations partially mitigated enzymatic inhibition, likely through competitive adsorption and reduced nanoplastic bioavailability. Overall, our findings demonstrate that NP-induced inhibition of soil enzymatic activity is widespread and primarily governed by particle size, exposure concentration, and soil properties. The identified 150 nm threshold should be interpreted as a data-driven hypothesis requiring further validation under environmentally realistic exposure scenarios rather than as a universal regulatory limit. Nevertheless, the integration of three-level meta-analysis with interpretable machine learning (SHAP) provides a robust and reproducible framework for identifying key toxicity drivers and supports future ecological risk assessment and evidence-based regulatory decision-making for nanoplastics.

ZnO is widely used as an electron transport layer in quantum-dot light-emitting diodes (QLEDs) because of its high electron mobility and suitable energy levels. However, rapid electron transport may cause excessive electron injection, leading to charge accumulation and parasitic recombination. SnO₂ provides weaker electron transport, but using it alone limits device performance. Here, a SnO₂/ZnO bilayer electron transport layer was introduced to regulate electron injection. Unlike previously reported structures in which SnO₂ directly contacts the QDs, the present configuration places SnO₂ on ITO and ZnO adjacent to the QD layer. The low-concentration ZnO overlayer reduced the RMS roughness of the SnO₂ film from 1.79 to 1.07 nm and facilitated electron injection, while the underlying SnO₂ layer moderated the electron supply. The bilayer also suppressed leakage current and showed the lowest capacitance peak, consistent with improved charge balance. The bilayer QLED achieved a maximum current efficiency of 13.40 cd/A and a maximum luminance of 33,210 cd/m². Its current efficiency was 100% and 32.7% higher than those of the SnO₂ and ZnO devices, respectively. These results demonstrate that the bilayer improves charge balance through facilitated electron injection and controlled electron supply.